Historically, in order to understand or appreciate a particular topic, one would need to read a myriad of resources and manually synthesize the contents of the resources. Conclusions or theories or broadly-categorized “results” could then be made based on this synthesis. This, of course, is a time-intensive and user-specific process.
However, as digital information becomes more and more prevalent and an increasing number of resources become available in a digital format in online databases, there is an opportunity to automate the reading and understanding of resources in order to derive useful knowledge across a wide variety of topics and for any generic user.
For example, the article Joint Learning of Ontology and Semantic Parser from Text by Starc and Mladenic from Jozef Stefan International Postgraduate School and published November 2015, describes a semantic parsing approach to analysis of digital content. However, this approach uses an ontology direction, which is the basis of supervised learning. The ontology defines the pathways or steps for reading the content. However, this approach requires supervisor intervention for the ontology direction.
In another example, the article Natural Language Processing (Almost) from Scratch by Collobert et al. from Journal of Machine Learning Research and published August 2011, describes an approach that utilizes neural networks based on supervised learning and requires a prior data set with predefined results for semantic understanding.
In another example, the article A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning by Collobert and Weston of NEC Labs America, describes an approach that also utilizes neural networks based on supervised learning.
Therefore, there is a need for systems and methods that provide for real-time, accurate, and verifiable identification and analysis of digital content that is more sophisticated than a basic key word analysis and which requires less supervision than existing systems.